Stocks-Data-Analysis-and-Visualization-in-Python

Stocks Data Analysis and Visualization in Python

1. Overview:

a. Goal:

• Created a funtion to perform portfolio analysis by calculating return, risk and Sharpe ratio
• The main problem was the huge manual effort required and the visualization of the results
• Reduced analysis time by 20%

b. Challenges Faced:

• Wrangling data of different stocks and combining them into a single dataframee.
• Missing values.
• Plotting QQ plot for stock modelling.

c. Interesting findings

• Graph of volatility clustering conveyed that stock volatility doent follow a normal distribution but actually a T- distribution
• High volatility regions lie close to each other and low volatility region lie next to each other
• We have to avoid stocks whose returns have negative skew because they will end up losing a lot of times
• Excessive kurtosis means more reward but that comes with a price of more risk.

Link to Github repository